It is a new paradigm to apply data mining technologies to analyze the crime groups and terrorist social networks,there is little work being done on analyzing the communication behavior of criminal and terrorist groups.This paper designed a simulation email system based on personality trait dimensions,called MEP,to model the email users' traffic behavior,proposed a new approach of computing the weight of each dimension in a personality trait vector by using personality trait judge matrix,and simulated the real-world email communication behavior based on normal distribution model satisfying users' personality trait.This paper proposed a social network analysis based algorithm called CNKM(Crime Network Key Member mining) to mine key members of a crime group,and employed time-series analysis techniques to discover the email sending and receiving rules in order to detect the abnormal communication cases.The experimental results show the efficiency and usability of the simulation email analysis system,the average simulation error is less than 10%,and demonstrate that CNKM is efficient.

It is hot topic to apply data mining techniques to anti-criminal and anti-terrorism research in many law enforcement agencies.The main contributions of this paper include:(1)designed a conceptual e-mail system(CEM)based on personality trait dimensions and intelligence attribute of e-mail users in order to model the e-mail traffic behavior of criminal and terrorist organizations;(2)used normal distribution controlled by the personality trait dimension and intelligence attribute to generate e-mail data;(3)used social network analysis and time-series visualization to search for interesting e-mail behavioral patterns and abnormal communications;(4)demonstrated that CEM has good robust and scalability,exactly simulate the behavior in e-mail's,and solve the problem of lacking simulation model of the e-mail system.

In order to recognize the false status which has been forged and tempered by suspects,a new method is proposed to compute attribute similarities based on tree edit distance,and its mathematical properties are proved. The paper proposes a new clustering algorithm based on hierarchical encoding method named HCTED(Hierarchical Clustering Algorithm Based on Tree Edit Distance). This method uses tree edit distance to compute attribute similarities with minimum cost,overcomes the shortage of traditional clustering algorithms and improves the precision of clustering according to the predefined threshold. Experiments demonstrate that the new method is accurate and efficient in identity recognition,discuss the effects of different experimental parameters,and show that HCTED is more accurate and faster than traditional clustering algorithms. The new algorithm has been used in data analysis of transient population for public security successfully.

The nonlinearity rectification is an important task to improving the sensor performance.This paper analyzes the sensors characteristics and designs a fitness function with interest in normal distribution and use boundary functions presents a new method named SGEP.The new method based on Gene Expression Programming is for solving the problem of the nonlinearity rectification in sensor system.The experimental result show that the new method is more effective and more flexible than traditional method.

SNA(Social Network Analysis) is a new hot spot in data mining area.This paper surveyed the current research advances in SAN,and introduced the author's three research results in recent years,including:(a) Mine the structure of virtual consortium,and discuss the formation of init virtual consortium,weight computation,the generation of the consortium tree and algorithm for structure mining of consortium.(b) Mine the core of consortium based on the Six Degrees of Separation and describe Shortest Path based on LINk wEight algorithm SPLINE(Shortest Path based on LINk wEight).(c) Mine the communication behaviors based on the user's personality,and set a model based on the data from 911 to simulate the email sending-and-receiving between the terrorists.

将数据挖掘技术应用于反犯罪和反恐怖是目前各国安全部门的研究热点。目前国内在分析犯罪和恐怖团伙之间联系行为等方面的研究工作有限。本文主要做了下列探索：（1）建立了一个可用的基于邮件用户个性特征和情报属性的概念仿真邮件系统CEM(Conceptual based EMail system)，模拟潜在的犯罪和恐怖组织利用电子邮件进行通信的规律;（2）利用符合个性特征和情报属性上的正态分布，模拟真实的邮件进行数据的收发；（3）使用社会网络分析和时间序列分析方法对邮件通信量进行深层次分析，挖掘有意义的邮件通信模式，进而发现异常通信行为；(4) 通过实验证明CEM系统具有很好的鲁棒性和伸缩性, 可以准确地模拟大量用户的邮件收发, 解决了目前仿真数据不足的缺点, 并用于发现不同性格特征群体收发邮件的规律。

本文探索一种基于社会网络分析技术的的挖掘社团核心成员的方法，要点是挖掘被监控社团网络的电子邮件通信规律。本文主要工作包括：（1）采用基于层次聚类算法将松散无规律的犯罪个体聚集为若干子网络；（2）提出一种基于六度分割定理的最短路径算法SPLINE（Shortest Path algorithm based on LINk wEight），（3）提出了基于SPLINE的犯罪集团网络核心挖掘算法KMM（Key-Member Mining）,(4) 通过实验证明了该算法优于传统的最短路径算法，可以极大缩减运行时间，减少搜索代价，对犯罪集团核心成员预测的准确率较高，大约为91.2%.

Since the incident about 9.11, the Security Sectors of many countries have put great attentions on gathering and mining of crime data and establishing anti-terrorist databases. With the emergence of anti-terrorist application, data mining for anti-terrorist has attracted great attention from both researchers and officers as well as in China. The purpose of analyzing and mining related terrorist or crimes data is that analyzing of psychology, behavior and related laws about crime, and providing hidden clues to prevent a criminal case, and forecasting terror happening to keeping with crime limits.